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Regular Paper

Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, China
School of Data Science, Fudan University, Shanghai 200433, China
Disney Research Pittsburgh, Pittsburgh 15241, U.S.A.

A preliminary version of the paper was published in the Proceedings of AAAI 2016.

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Abstract

Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time-consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, which utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements, are learned and inferred from data. During the inference stage, the maximum a posterior (MAP) estimation framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.

Keywords

graphical designlayout automationprobabilistic graphical model

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Journal of Computer Science and Technology
Volume 34 Issue 1,
January 2019
Pages 155-169
DOI: 10.1007/s11390-019-1904-1
Cite this article:
Qiang Y-T, Fu Y-W, Yu X, et al. Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models. Journal of Computer Science and Technology, 2019, 34(1): 155-169. https://doi.org/10.1007/s11390-019-1904-1

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Received: 14 January 2018
Revised: 12 November 2018
Published: 18 January 2019
©2019 Springer Science + Business Media, LLC & Science Press, China
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